IBM Research AI

IBM Research has been exploring artificial intelligence and machine learning technologies and techniques for decades. We believe AI will transform the world in dramatic ways in the coming years – and we’re advancing the field through our portfolio of research focused on three areas: AI Science, AI Engineering, and AI Tech. We’re also working to accelerate AI research through collaboration with like-minded institutions and individuals to push the boundaries of AI faster – for the benefit of industry and society.

Working to make it easier and more seamless to build scalable AI models, capabilities, and tools. This work brings together researchers and engineers with deep expertise in applied AI, distributed systems, visualization, and AI model tuning.

AI Tech

Mastering core capabilities in AI, including natural language processing, speech and image recognition, and the fundamentals of neural networks, knowledge representation and reasoning.

AI Science

Expanding the frontiers of artificial intelligence by creating core algorithmic and neural network advancements. This research addresses the full spectrum of topics related to machine intelligence and deriving knowledge from data, including: learning, reasoning, the mathematics of AI, the physics of AI, and AI for shared prosperity.

Featured projects

Featured projects

Distributed deep learning software record

IBM Research AI experts have created distributed deep learning software, achieving record performance for image recognition accuracy and large neural networks composed of up to 250 GPUs, a special processor for large amounts of data.

In-memory computing

IBM Research scientists have demonstrated that an unsupervised machine-learning algorithm, running on one million phase change memory (PCM) devices, successfully found temporal correlations in unknown data streams.

First movie trailer created by AI

20th Century Fox turned to IBM Research to help make a new trailer for its film, "Morgan." Using experimental Watson APIs, the team analyzed visuals, audio and composition from 100 horror movie trailers to select the best moments for the first A.I.-generated movie trailer.

Creating music with AI

Watson Beat is a machine deep learning algorithm that learns to create music. IBM researchers are teaching a complex neural network to understand music theory, structure (pitch, time signature, key signature), emotional intent and co-create music with a human partner.

Using deep learning to forecast ocean waves

Scientists have made amazing advances enabling machines to understand language and process images for such applications as facial recognition, image classification (e.g., “cat” or “dog”) and translation of texts. Work in the IBM Research lab in Dublin this summer was focused on a very different problem: using AI techniques such as deep learning to forecast a physical process, namely, ocean waves.

Distributed deep learning software record

IBM Research AI experts have created distributed deep learning software, achieving record performance for image recognition accuracy and large neural networks composed of up to 250 GPUs, a special processor for large amounts of data. Developers and data scientists can now preview this technical milestone in version 4 of the PowerAI enterprise deep learning software.

In-memory computing

IBM Research scientists have demonstrated that an unsupervised machine-learning algorithm, running on one million phase change memory (PCM) devices, successfully found temporal correlations in unknown data streams. When compared to state-of-the-art classical computers, this prototype technology is expected to yield 200x improvements in both speed and energy efficiency, making it highly suitable for enabling ultra-dense, low-power, and massively-parallel computing systems for applications in AI.

First movie trailer created by AI

Sixty-five percent of movie-goers watch trailers on YouTube to help them pick a movie. 20th Century Fox turned to IBM Research to help make a new trailer for its film, "Morgan." Using experimental Watson APIs, the team analyzed visuals, audio and composition from 100 horror movie trailers to select the best moments for the first A.I.-generated movie trailer.

Creating music with AI

Watson Beat is a machine deep learning algorithm that learns to create music. IBM researchers are teaching a complex neural network to understand music theory, structure (pitch, time signature, key signature), emotional intent and co-create music with a human partner. Taking in audio files or live play, Watson Beat generates unique song combinations in line with music theory in audio file, or sheet music (notes) format.

Using deep learning to forecast ocean waves

Moments represents a significant milestone in tackling the challenge of action recognition, an important first step in helping computers understand activities which can ultimately be used to describe complex events (e.g. changing a tire), and is the only dataset to cover human, animal or object-centric activities. It also understands actions across domains, that represent a dynamic event at different levels of abstraction, for example ‘opening’ could be used to describe the opening of a book, a flower or a person’s eyes.

New model for identifing unfamiliar objects

Applications of AI are quickly becoming ubiquitous, powered by algorithms that learn from large amounts of data. Humans, on the other hand, learn very differently: they are able to reason based on a small number of assumptions and a set of logical rules. Our IBM Research team designed a method capable of combining these two learning styles, augmenting large data sets with structured human-generated knowledge and logical rules to improve performance of visual recognition.

IBM Research AI @ ICLR

IBM Research is a pioneer across many aspects of AI. At the 6th International Conference on Learning Representations (ICLR 2018), our team will share recent discoveries in learning data representations, techniques that are key to the success of machine learning algorithms. These techniques enable machine learning systems to automatically discover how to represent raw data for subsequent analysis. Learning data representations is an important learning task that powers computer vision, speech recognition, natural language processing, drug design, and other advances in AI. At ICLR 2018, IBM Research will present technical papers on adversarial learning, self-organizing networks for multi-task learning, open-domain question answering, disentanglement of representations, reinforcement learning, and deep learning for graphical data. Below are details on the papers IBM Research will be presenting at ICLR 2018.